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Found 4 papers, 1 papers with code
A Framework for Partially Observed Reward-States in RLHF
We show reductions from the the two dominant forms of human feedback in RLHF - cardinal and dueling feedback to PORRL.
Double Descent and Overfitting under Noisy Inputs and Distribution Shift for Linear Denoisers
Motivated by this, we study supervised denoising and noisy-input regression under distribution shift.
Offline Policy Evaluation and Optimization under Confounding
Evaluating and optimizing policies in the presence of unobserved confounders is a problem of growing interest in offline reinforcement learning.
Learning Mixtures of Markov Chains and MDPs
We present an algorithm for learning mixtures of Markov chains and Markov decision processes (MDPs) from short unlabeled trajectories.
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